The nature of feedback: How different types of peer feedback affect writing performance
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Feedback quality is strongly associated with specificity.
“This is consistent with the research of Nelson and Schunn (2009); Henderson et al. (2019), who found that feedback quality is strongly associated with specificity.”
From Impact of AI assistance on student agency (2024) · cites this paper as Nelson and Schunn (2009)
Studies have found that the affective features of feedback have less impact on student improvement than cognitive features.
“That students used the improvement category kindness to a lesser extent than the improvement suggestions specificity, justification, and constructivity, resonates well with studies that have found that the affective features of feedback has less impact on student improvement than cognitive features (Hattie & Timperley, 2007;Nelson & Schunn, 2009).”
From Using learning analytics to understand student perceptions of peer feedback (2021) · cites this paper as Nelson & Schunn, 2009
Student's sensemaking of the feedback correlates with their experience of its usefulness is known from previous studies and relates to the problem of the feedback gap.
“That student's sensemaking of the feedback correlates with their experience of its usefulness is known from previous studies and relates to the problem of the feedback gap (Jonsson, 2013;Nelson & Schunn, 2009).”
From Using learning analytics to understand student perceptions of peer feedback (2021) · cites this paper as Nelson & Schunn, 2009
In a study of peer feedback using 1,073 feedback segments from an online peer review system (SWoRD), Nelson and Schunn (2009) found that students' comprehension of the feedback was the only significant mediator for student implementation.
“in a study of peer feedback using 1,073 feedback segments from an online peer review system (SWoRD), Nelson and Schunn (2009) found that student's comprehension of the feedback was the only significant mediator for student implementation.”
From Using learning analytics to understand student perceptions of peer feedback (2021) · cites this paper as Nelson and Schunn (2009)
Nelson and Schunn (2009) found that students were more likely to understand feedback if it offered concrete solutions, a location of the problem(s), or a summary.
“In particular, they found that students were more likely to understand the feedback if it offered concrete solutions, a location of the problem(s), or if the feedback included a summary.”
From Using learning analytics to understand student perceptions of peer feedback (2021) · cites this paper as Nelson and Schunn (2009)
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- Authors
- Nelson, M. M., & Schunn, C. D.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Nelson, Melissa M.
- ORCID
- Schunn, Christian D.
- Venue
- Instructional Science
- Published
- 2008-04-04
- Volume
- 37
- Issue
- 4
- Pages
- 375-401
- ISSN
- 0020-4277 (ISSN-L)
- 1573-1952
- OpenAlex
- W2075371609
- Cited by
- 602
- References
- 67
- Citations by year
- 2026: 40 · 2025: 48 · 2024: 53 · 2023: 54 · 2022: 42 · 2021: 50 · 2020: 36 · 2019: 37 · 2018: 40 · 2017: 26 · 2016: 32 · 2015: 31 · 2014: 27 · 2013: 32 · 2012: 22
- FWCI
- 46.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Instructional Science (publishedVersion)
- D-Scholarship@Pitt (University of Pittsburgh) (acceptedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- peer feedback, writing performance, feedback implementation, problem understanding, peer review, swords
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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